The Statistics and Data Management Core (Stats/DM Core) 1) provides statistical support to investigators conducting Center research studies and 2) maintains the databases required by projects. Core and Project investigators collaborate on experimental design, statistical modeling and analyses, and statistical graphics and presentations in Projects 1-5. These capabilities include design support ranging from broad conceptual support to detailed power calculations; analytic support ranging from advice on appropriate statistical models to conducting complex statistical analyses; and presentation support ranging from graphical advice to co- authoring papers and abstracts. In addition, the Stats/DM Core will develop new statistical methodology to enhance the research conducted in the Center, and provide training opportunities for graduate students in statistics and all levels of trainees in neuroscience and psychiatric research. The Stats/DM Core data manager will work with the Department of Psychiatry's Office of Academic Computing and Center investigators to develop databases that facilitate the integration of results across projects (e.g., organized and retrievable databases for mRNA, miRNA, functional annotation and regulatory network data from P1&P3). The Stats/DM Core's statistical support will be provided by Dr. Allan Sampson (Core Director), and Drs. Kehui Chen and George C. Tseng (Core Co-Investigators). Consultative support will be available from Dr. Satish Iyengar and Dr. Lisa Weissfeld. Senior PhD Graduate Student Researchers will also provide detailed computations and analyses under the supervison of Core faculty. When projects could benefit from access to additional statistical expertise from the greater Pittsburgh statistical community, including other departments at the University of Pittsburgh and Carnegie-Mellon University, the Stats/DM Core will serve as a referral and coordination resource for Center investigators. RELEVANCE (See instructions): This Core will develop innovative statistical methodology when currently available statistical methods are not fully effective. New statistical methodology could provide advances beyond current statistical methods that can be utilized in other areas of translational mental health research, in addition to increasing the impact of studies conducted by Center investigators.